Author

Kishan Gurram

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 4

Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 3 Approach:machine learning (classical)· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Assay:protein structure· 1 Approach:simulation· 1 Research Focus:software / tool· 1 Approach:statistical model· 1

Frequent Co-Authors

Ben Geoffrey A SiD· 4 shared Nagaraj M. KulkarniiD· 4 shared Deepak AgrawaliD· 3 shared Rajappan Vetrivel· 3 shared Abhishek Singh· 1 shared Nivedita BhartiiD· 1 shared Samir Anapat· 1 shared Sowmya Kanchan· 1 shared

Activity by Year

2022-2026 · peak 2/yr

Co-Author Collaboration Network

8 top collaborators · 19 mutual co-author links

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4 shared papersBioinformatics

Ben Geoffrey A SORCID

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Author profile →

4 papers

Recent·Most cited

DMPKformer: An Interpretable Multimodal Deep Learning Framework for Reliable ADMET Property Prediction

Ben Geoffrey A S, Abhishek Singh, Sowmya Kanchan, Samir Anapat, Kishan Gurram, Nagaraj M. Kulkarni

DMPKformer predicts ADMET properties using a multimodal deep learning framework that integrates molecular representations, achieving competitive performance and interpretability.

bioRxiv · May 29, 2026 · PreprintBioinformatics

PROTAC-Design-Evaluator (PRODE) -- An Advanced Method for in-silico PROTAC design

Ben Geoffrey A S, Deepak Agrawal, Nagaraj M. Kulkarni, Rajappan Vetrivel, Kishan Gurram

This paper introduces a computational method for in-silico PROTAC design and evaluation, demonstrating its ability to predict PROTAC binding modes and thermodynamics for known and novel target systems.

bioRxiv · Jun 12, 2023 · PreprintBioinformatics

An in-silico approach for novel molecular glue design by rationalizing known molecular glue mediated ternary complex formation

Ben Geoffrey A S, Nagaraj M. Kulkarni, Deepak Agrawal, Nivedita Bharti, Rajappan Vetrivel, Kishan Gurram

This paper establishes an in-silico rationale for molecular glue design by analyzing a known case and discusses its application in designing novel molecular glues using AI techniques.

bioRxiv · Oct 4, 2022 · PreprintBioinformatics

A New In-Silico Approach for PROTAC Design and Quantitative Rationalization of PROTAC mediated Ternary Complex Formation

Ben Geoffrey A S, Nagaraj M. Kulkarni, Deepak Agrawal, Rajappan Vetrivel, Kishan Gurram

This paper presents an in-silico approach for PROTAC design that uses AI and molecular dynamics to predict ternary complex stability and quantify PROTAC performance metrics.

bioRxiv · Jul 11, 2022 · PreprintBioinformatics